Group verifiable secure aggregate federated learning based on secret sharing.

Federated learning is a distributed machine learning approach designed to tackle the problems of data silos and the security of raw data. Nevertheless, it remains susceptible to privacy leakage risks and aggregation server tampering attacks. Current privacy-preserving methods often involve significant computational and communication overheads, which can be challenging in resource-limited settings, hindering their practical application. To overcome these obstacles, this article proposes an efficient secure aggregation scheme based on secret sharing-GVSA. GVSA safeguards the privacy of local models through a masking technique and improves the system's resilience to user dropouts by utilizing secret sharing. Furthermore, GVSA implements a dual aggregation approach and incorporates lightweight validation tags to verify the accuracy of the aggregation results. By adopting a grouping strategy, GVSA effectively minimizes the computational burden on both users and the server, making it well-suited for resource-constrained environments. We compare GVSA with leading existing methods and assess its performance through various experimental setups. Experimental results demonstrate that GVSA maintains high security while effectively preserving model accuracy. Compared to FedAvg, GVSA incurs only approximately 7% additional computational overhead. Furthermore, compared to other secure aggregation schemes with the same security level, GVSA achieves approximately a 2.3× improvement in training speed.